Classification of Acoustic Emission Signals from an Aluminum Pressure Vessel Using a Self-Organizing Map

Acoustic emission nondestructive testing has been used for real-time monitoring of complex structures. All of the structures were made of materials at least 0.070 inch thick. The purpose of this research was to demonstrate the feasibility of using neural networks to classify acoustic emission signals gathered from a pressure vessel made of 2024-T3 aluminum 0.040 inches thick, i.e. thin aluminum sheet. AE waveforms were recorded during fatigue cycling of one pressure vessel using a wide band transducer and a digital oscilloscope connected to a computer. The source for each signal was determined using two narrow band transducers and a LOCAN-AT data acquisition system. The power spectrum was calculated for each waveform. A Kohonen self-organizing map (SOM) was used to cluster the spectra. The network clustered the data on a two-dimensional feature space according to the source of the signal. A total of 3,600 power spectra were used to train the neural network, and 1,800 were used to test the network. Initially there was overlap between the clusters on the two-dimensional feature space; however, this was found to be due to human error. The SOM itself correctly classified all of the signals

Unsupervised anomaly detection in pressurized water reactor digital twins using autoencoder neural networks

Deep learning (DL), that is becoming quite popular for prediction and analysis of complex patterns in large amounts of data is used to investigate the safety behaviour of the nuclear plant items. This is achieved by using multiple layers of artificial neural networks to process and transform input data, allowing for the creation of highly accurate predictive models. Particularly to the aim the unsupervised machine learning approach and the digital twin concept in form of pressurized water reactor 2-loop simulator are used. This innovative methodology is based on neural network algorithm that makes capable to predict failures of plant structure, system, and components earlier than the activation of safety and emergency systems. Moreover, to match the objective of the study several scenarios of loss of cooling accident (LOCA) of different break size were simulated. To make the acquisition platform realistic, Gaussian noise was added to the input signals. The neural network has been fed by synthetic dataset provide by PCTRAN simulator and the efficiency in event identification was studied. Further, due to the very limited studies on the unsupervised anomaly detection by means of autoencoder neural networks applied for plant monitoring and surveillance, the methodology has been validated with experimental data from resonant test rig designed for fatigue testing of tubular components. The obtained results demonstrate the reliability and the efficiency of the methodology in detecting anomalous events prior the activation of safety system. Particularly, if the difference between the expected readings and the collected data goes beyond the predetermined threshold, then the anomalous event is identified, e.g., the model detected anomalies up to 38 min before the reactor scram intervention

Solar Storm Type Classification Using Probabilistic Neural Network compared with the Self-Organizing Map

One of the task of the LAPAN is making observation and forecasting of solar storms disturbance. This disturbances can affect the earths electromagnetic field that disrupt the electronic and navigational equipment on earth. LAPAN wanted a computer application that can automatically classify the type of solar storms, which became part of early warning systems to be created. The classification of the digital images of solar storm / sunspot is based on Modified - Zurich Sunspot Classification System. Classification method that we use here is the Probabilistic Neural Networks. The result of testing is promising because it has an accuracy of 94 for testing data. The accuracy is better than the accuracy of similar applications weve built with a combination of methods Self-Organizing Map and K-Nearest Neighbor

Field test of multi-hop image sensing network prototype on a city-wide scale

Open Access funded by Chongqing University of Posts and Telecommuniocations Under a Creative Commons license, https://creativecommons.org/licenses/by-nc-nd/4.0/Wireless multimedia sensor network drastically stretches the horizon of traditional monitoring and surveillance systems, of which most existing research have utilised Zigbee or WiFi as the communication technology. Both technologies use ultra high frequencies (mainly 2.4 GHz) and suffer from relatively short transmission range (i.e. 100 m line-of-sight). The objective of this paper is to assess the feasibility and potential of transmitting image information using RF modules with lower frequencies (e.g. 433 MHz) in order to achieve a larger scale deployment such as a city scenario. Arduino platform is used for its low cost and simplicity. The details of hardware properties are elaborated in the article, followed by an investigation of optimum configurations for the system. Upon an initial range testing outcome of over 2000 m line-of-sight transmission distance, the prototype network has been installed in a real life city plot for further examination of performance. A range of suitable applications has been proposed along with suggestions for future research.Peer reviewe

Application of Digital Image Segmentation of Plantation Fruit Classification in Samarinda Agricultural Polytechnic

Applications of Digital Image Segmentation of Plantation Fruit Classification in Samarinda State Agricultural Polytechnic Based on Form The development of computer technology at this time has brought significant progress in various aspects of human life. Such development is supported by the availability of increasingly high hardware and software, one of the technologies experiencing rapid development is image processing. Image processing is a system where the process is carried out by entering an image and the result is also an image. Currently the use of digital images is widely used in various fields one of which is in the plantation sector. Therefore, the purpose of this study is to create a digital image segmentation application for the classification of plantation fruit based on shape. The method used for image segmentation is the Thresholding method, while the image classification uses the Artificial Neural Network (ANN) method. The accuracy generated by the system both in the training process and testing shows that the method used can classify fruit images wel

A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper

Distributed storage manager system for synchronized and scalable AV services across networks

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2011 Hindawi Publishing CorporationThis paper provides an innovative solution, namely, the distributed storage manager that opens a new path for highly interactive and personalized services. The distributed storage manager provides an enhancement to the MHP storage management functionality acting as a value added middleware distributed across the network. The distributed storage manager system provides multiple protocol support for initializing and downloading both streamed and file-based content and provides optimum control mechanisms to organize the storing and retrieval of content that are remained accessible to other multiple heterogeneous devices